Patient flow modeling is a growing field of interest in health services research. Several techniques have been applied to model movement of patients within and between health-care facilities. However, individual patient experience during the delivery of care has always been overlooked. In this work, a random effects model is introduced to patient flow modeling and applied to a London Hospital Neonatal unit data. In particular, a random effects multinomial logit model is used to capture individual patient trajectories in the process of care with patient frailties modeled as random effects. Intuitively, both operational and clinical patient flow are modeled, the former being physical and the latter latent. Two variants of the model are proposed, one based on mere patient pathways and the other based on patient characteristics. Our technique could identify interesting pathways such as those that result in high probability of death (survival), pathways incurring the least (highest) cost of care or pathways with the least (highest) length of stay. Patient-specific discharge probabilities from the health care system could also be predicted. These are of interest to health-care managers in planning the scarce resources needed to run health-care institutions.

Is the NHS in England too big to fail?Dalton, S., Chahed, S. and Chaussalet, T.J. 2016. Is the NHS in England too big to fail? 8th Institute of Mathematics and Its Applications. Asia House, London 21 Mar 2016

Modelling high dependency care in the local neonatal unitDalton, S. and Chaussalet, T.J. 2011. Modelling high dependency care in the local neonatal unit. in: Operational Research Information National Health Policy: proceedings of the 37th ORAHS conference Cardiff School of Mathematics, Cardiff University.

Developing an application of an accident and emergency patient simulation modelling using an interactive frameworkCodrington-Virtue, A., Chaussalet, T.J., Whittlestone, P. and Kelly, J. 2007. Developing an application of an accident and emergency patient simulation modelling using an interactive framework. in: Brailsford, S. and Harper, P.R. (ed.) Operational research for health policy: making better decisions: proceedings of the 31st Annual Conference of the European Working Group on Operational Research Applied to Health Services Oxford ; New York Peter Lang. pp. 61-76